Analysis of Labor Force Participation

Data analysis task and coding sample

Author

George Rao

Introduction

This page is a revised version of a “data task” I completed for a workshop related to careers in social science research. I am including a link to this page as a coding sample in my applications to pre-doctoral research assistantships.

The structure of this page is based on a PDF of instructions (link in the right-hand margin). The instructions are organized into two parts, each with a set of questions about a provided dataset on labor force participation. This page answers each of those questions with supporting charts and tables.
This entire page is written in R and Markdown using Quarto, and the source code is available on GitHub (links in the right-hand margin).
The source code snippets for the figures on this page are hidden within toggles, which can be expanded and folded up as needed.
Many of the figures on this page are grouped in tabs for ease of presentation. Click on a tab to switch to a different figure in that group.

Part 1: Labor Force Participation

Question 1

How has female labor force participation evolved since 1994?

Let’s chart labor force participation (LFP) over time by sex to observe how it has evolved for women over time compared to men. Since this set of questions will focus on women older than 25, it’s also worth looking at LFP over time split before and after that age milestone.

Load packages, import data, and reorder factors

library(tidyverse)
library(scales)
library(DescTools)
library(gt)
theme_set(theme_bw())

raw_data <- read_csv("data/cps_women_lfp.csv")

# Reorder factors
data <- raw_data |>
  mutate_if(is_character, as_factor) |>
  mutate(
    education = fct_relevel(
      education, "< HS Diploma", "HS Diploma",
      "Some college, no degree", "Associate's Degree",
      "Bachelor's Degree", "Master's or Higher"
    ),
    age = fct_relevel(
      age, "< 25", "25-34", "35-44", "45-54",
      "55-64", "65-74", "75+"
    ),
    wageinc_quantiles = fct_relevel(
      wageinc_quantiles, "0-19.99", "20-39.99",
      "40-59.99", "60-79.99", "80-100"
    ),
    income_quantiles = fct_relevel(
      income_quantiles, "0-19.99", "20-39.99",
      "40-59.99", "60-79.99", "80-100"
    )
  )

Create new variables and filter dataset

lfp <- data |>
  # Since LFP is the variable of interest, filter out NAs
  filter(!is.na(lfp)) |>
  # Create new variables
  mutate(
    lfp_lgl = lfp == "In labor force",
    college_lgl = college == "Has college degree",
    # This logical is true if we know the individual is self-employed,
    # but false otherwise, including if there is a missing value
    self_employed_lgl = if_else(self_employed == "Self-employed",
      TRUE, FALSE, FALSE
    ),
    lfp_lgl_excl_self = !self_employed_lgl & lfp_lgl,
    employed_lgl = employed == "Employed",
    lfp_lgl = lfp == "In labor force",
    covid_tw_lgl = covid_telework == "Telework from 2021-2022 due to COVID"
  ) |>
  mutate(
    .by = cpsidp,
    # Two lines needed since calculations by cpsidp are expensive
    missing = all(is.na(covid_tw_lgl)),
    had_telework = any(covid_tw_lgl, na.rm = TRUE)
  ) |>
  mutate(
    had_telework = if_else(missing, NA, had_telework)
  ) |>
  select(!missing)

# Create filtered data set of women only
women <- lfp |>
  filter(sex == "Female")

# Create filtered data set of women over 25 only
women_over_25 <- women |>
  filter(age != "< 25" & !is.na(age))

Create functions to streamline code

plot_mean_by_group_over_time <- function(data, var, group) {
  data |>
    summarize(
      .by = c(year, {{ group }}),
      mean = weighted.mean({{ var }}, wgt, na.rm = TRUE)
    ) |>
    ggplot(aes(x = year, y = mean, color = {{ group }})) +
    geom_line(alpha = 0.75) +
    geom_point(alpha = 0.75) +
    labs(x = "Year")
}

plot_props_over_time <- function(data, group) {
  data |>
    summarize(
      .by = c(year, {{ group }}),
      wgt_count = sum(wgt)
    ) |>
    mutate(
      .by = c(year),
      prop = wgt_count / sum(wgt_count)
    ) |>
    ggplot(aes(x = year, y = prop, color = {{ group }})) +
    geom_line(alpha = 0.75) +
    geom_point(alpha = 0.75) +
    labs(x = "Year", y = "Proportion") +
    scale_y_continuous(labels = percent)
}

Code

lfp |>
  plot_mean_by_group_over_time(lfp_lgl, sex) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Female labor force participation hit a peak in 2000",
    subtitle = "Male LFP has been falling nearly continuously since 2000",
    y = "Labor force participation rate",
    color = "Sex"
  )

Code

women |>
  filter(!is.na(age)) |>
  mutate(
    over_25 = if_else(age != "< 25", ">= 25", age)
  ) |>
  plot_mean_by_group_over_time(lfp_lgl, over_25) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Younger women have participated less in the labor force since 2000",
    subtitle = "The trend seems to be reversing in recent years, since COVID in 2020",
    y = "Labor force participation rate",
    color = "Age group"
  )

Code

women_over_25 |>
  plot_props_over_time(age) +
  labs(
    title = "The population of women is aging",
    subtitle = "Significant rise in fraction of women 65+",
    color = "Age group"
  )

Female labor force participation increased from 1994 to the early 2000s, then fell until the early 2020s. Since then, it has risen back to 1994 levels. At the same time, however, male labor force participation has fallen by even more since the early 2000s and has not recovered as much.

Perhaps this is due to the aging of the population as a whole, as can be seen in the chart of age group proportions.

Question 2

Among women older than 25, which groups (race, age, income percentile, etc.) of people had the biggest changes in labor force participation since 1994?

Let’s chart LFP over time by each group first to observe general trends.

Create function to streamline code

chg_by_group <- function(data, var, group, initial_year, final_year) {
  data |>
    summarize(
      .by = {{ group }},
      initial = weighted.mean(
        if_else(year == initial_year, {{ var }}, NA), wgt,
        na.rm = TRUE
      ),
      final = weighted.mean(
        if_else(year == final_year, {{ var }}, NA), wgt,
        na.rm = TRUE
      ),
      chg = final - initial
    ) |>
    arrange(desc(chg)) |>
    gt() |>
    opt_align_table_header(align = "left") |>
    cols_align(align = "left", columns = {{ group }}) |>
    cols_label(
      initial = initial_year,
      final = final_year,
      chg = "Change"
    ) |>
    fmt_percent() |>
    sub_missing(columns = initial:chg, missing_text = "") |>
    tab_options(table.align = "left")
}

Code

women_over_25 |>
  plot_mean_by_group_over_time(lfp_lgl, race) +
  facet_wrap(vars(race), ncol = 3) +
  theme(legend.position = "none") +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Black women have the highest LFP rate",
    subtitle = "The rate for white women (the largest group) is the lowest",
    y = "Labor force participation rate",
    color = "Race"
  )

Code

women_over_25 |>
  plot_mean_by_group_over_time(lfp_lgl, age) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Women aged 25-54 have LFP rates approaching 80%",
    subtitle = "Labor force participation declines quickly after age 55",
    y = "Labor force participation rate",
    color = "Age group"
  )

Code

women_over_25 |>
  plot_mean_by_group_over_time(lfp_lgl, income_quantiles) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Women in the highest income quintiles have the highest LFP rate",
    subtitle = "The effect may be reciprocal as working also causes higher income",
    y = "Labor force participation rate",
    color = "Income quintile"
  )

Code

women_over_25 |>
  plot_mean_by_group_over_time(lfp_lgl, education) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "The more educated a woman, the higher her expected LFP rate",
    subtitle = "The effect is monotonic for nearly all years by increasing education level",
    y = "Labor force participation rate",
    color = "Education level"
  )

Next, let’s calculate the specific increases and decreases since 1994 to observe the groups that experienced the largest changes in LFP.

Code

women_over_25 |>
  chg_by_group(lfp_lgl, race, 1994, 2024) |>
  cols_label(1 ~ "Race") |>
  tab_header(
    title = "Hispanic women experienced the biggest rise in LFP",
    subtitle = "White women experienced the biggest fall"
  )

Hispanic women experienced the biggest rise in LFP
White women experienced the biggest fall
Race	1994	2024	Change
Hispanic	54.38%	60.71%	6.32%
Black	61.47%	63.99%	2.52%
Asian or Pacific Islander	59.47%	61.26%	1.79%
Native American	59.18%	60.03%	0.85%
White	58.58%	56.99%	−1.60%
NA	54.77%
Two or more		67.76%

Code

women_over_25 |>
  chg_by_group(lfp_lgl, age, 1994, 2024) |>
  cols_label(1 ~ "Age group") |>
  tab_header(
    title = "Working into old age has become increasingly common",
    subtitle = "Women aged 55-74 experienced the biggest rise in LFP"
  )

Working into old age has become increasingly common
Women aged 55-74 experienced the biggest rise in LFP
Age group	1994	2024	Change
55-64	49.06%	61.67%	12.61%
65-74	13.20%	24.19%	10.99%
25-34	73.84%	77.99%	4.15%
45-54	74.68%	77.13%	2.45%
35-44	77.14%	79.38%	2.24%
75+	3.46%	5.58%	2.12%

Code

women_over_25 |>
  # Since the 2024 data is not yet available, compare with the 2023 rate
  chg_by_group(lfp_lgl, income_quantiles, 1994, 2023) |>
  cols_label(1 ~ "Income quintile") |>
  tab_header(
    title = "The lowest earners experienced the biggest drop in LFP",
    subtitle = "The highest quintile of female earners were relatively unaffected"
  ) |>
  tab_footnote(
    footnote = "The 2023 rate is used for comparison as 2024 income data is not yet available.",
    locations = cells_column_labels(3)
  )

The lowest earners experienced the biggest drop in LFP
The highest quintile of female earners were relatively unaffected
Income quintile	1994	2023¹	Change
NA	58.66%	58.68%	0.02%
80-100	90.65%	89.28%	−1.37%
60-79.99	88.84%	83.48%	−5.37%
40-59.99	77.10%	70.71%	−6.39%
0-19.99	26.38%	18.06%	−8.32%
20-39.99	44.86%	35.77%	−9.09%
¹ The 2023 rate is used for comparison as 2024 income data is not yet available.

Code

women_over_25 |>
  chg_by_group(lfp_lgl, education, 1994, 2024) |>
  cols_label(1 ~ "Education level") |>
  tab_header(
    title = "Women without a high school diploma have gained the most",
    subtitle = "This balks the overall trend"
  )

Women without a high school diploma have gained the most
This balks the overall trend
Education level	1994	2024	Change
< HS Diploma	29.19%	33.59%	4.40%
Bachelor's Degree	73.82%	69.69%	−4.13%
Master's or Higher	79.87%	71.73%	−8.14%
HS Diploma	57.41%	48.94%	−8.46%
Some college, no degree	66.91%	55.83%	−11.08%
Associate's Degree	74.22%	62.78%	−11.44%

Hispanic women, older women, and women without a high school diploma experienced the biggest rises in LFP while white women, women in the lowest income quintiles, and women with an associate’s degree experienced the biggest falls.

Question 3

Use the data to examine trends among women older than 25 for each of the following factors from 1994 to 2024: wage and salary income, social insurance income, and education attainment. Based on these trends, what factors could be driving the patterns you found in Questions 1 and 2?

Let’s examine wage and salary income by group. For this analysis, it makes sense to filter out women who are not earning a wage or salary; otherwise, the total effect will a mix of changes in wages and changes in the proportion of employed women, and we only want to see the changes in wages.

Create function to streamline code

plot_income_over_time_by_group <- function(data, income_var, group) {
  data |>
    filter({{ income_var }} != 0) |>
    summarize(
      .by = c(year, {{ group }}),
      mean_income = weighted.mean({{ income_var }}, wgt, na.rm = TRUE)
    ) |>
    filter(!is.na({{ group }})) |>
    ggplot(aes(x = year, y = mean_income, color = {{ group }})) +
    geom_line(alpha = 0.75) +
    geom_point(alpha = 0.75) +
    scale_y_continuous(labels = dollar) +
    labs(x = "Year")
}

Code

women_over_25 |>
  plot_income_over_time_by_group(income) +
  labs(
    title = "Average earned income for income-earning women has increased",
    subtitle = "Women earning an income earned about $20,000 more in 2020 than in 1994",
    y = "Average earned income"
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(income, race) +
  labs(
    title = "Average earned income has increased across all races",
    subtitle = "However, White and AAPI women have seen the largest increases",
    y = "Average earned income",
    color = "Race"
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(income, age) +
  labs(
    title = "Increases in income have been level across age groups",
    subtitle = "The average working elderly woman is earning double what she earned in 1994",
    y = "Average earned income",
    color = "Age group"
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(income, education) +
  labs(
    title = "The more education a woman has, the higher her expected income",
    subtitle = "This trend holds across all years and education levels",
    y = "Average earned income",
    color = "Education level"
  )

Average income has increased across the board. The most relevant observation is that income has increased the most for women aged 65 and older who are earning an income. This is not due to the effect of more elderly women working (those not working have been filtered out), but that the average working elderly woman is earning a higher income. This may be related to the increase in LFP for women over 65, with the logic that higher wages are associated with higher LFP, but it’s hard to say that this relationship holds for all groups. For instance, Asian or Pacific Islander women experienced a dramatic increase in average income but had almost no change in LFP since 1994.

Now, let’s examine social insurance income. It also makes sense to filter out women not receiving social insurance income from the analysis.

Code

women_over_25 |>
  plot_income_over_time_by_group(incss) +
  labs(
    title = "Average social insurance income (SII) has increased since 1994",
    subtitle = "SII increased sharply during the Great Recession and in 2020 during COVID"
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(incss, race) +
  facet_wrap(vars(race), ncol = 3) +
  theme(legend.position = "none") +
  labs(
    title = "White women have received the highest average SII over time",
    subtitle = "The rise in SII has been approximately similar across races",
    y = "Average social insurance income",
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(incss, age) +
  facet_wrap(vars(age), ncol = 3) +
  theme(legend.position = "none") +
  labs(
    title = "Average SII has increased sharply for women 55 to 74",
    subtitle = "It has increased to a lesser extent for other age groups",
    y = "Average social insurance income"
  )

Code

women_over_25 |>
  plot_income_over_time_by_group(incss, education) +
  labs(
    title = "Social income increases with education",
    subtitle = "The effect is large, around $1,000 for each level of education",
    y = "Average social insurance income",
    color = "Education level"
  )

The results are largely the same as for earned income, except for education, where there is perhaps an unexpected explanation: since unemployment benefits are often based on recent earnings, highly educated women receive more on average.

Before moving on to education attainment, let’s compare the two kinds of income we just analyzed:

Code

women_over_25 |>
  mutate(
    across(inctot:income, \(x) if_else(x == 0, NA, x))
  ) |>
  summarize(
    .by = year,
    across(
      .cols = inctot:income,
      .fns = list(
        mean = \(x) weighted.mean(x[!is.na(x)], wgt[!is.na(x)]),
        median = \(x) Quantile(x[!is.na(x)], wgt[!is.na(x)], 0.5)
      )
    )
  ) |>
  pivot_longer(
    cols = inctot_mean:income_median,
    names_sep = "_",
    names_to = c("income_type", "stat"),
    values_to = "value"
  ) |>
  ggplot(aes(x = year, y = value, color = income_type, linetype = stat)) +
  scale_y_continuous(labels = dollar) +
  geom_line(alpha = 0.75) +
  geom_point(alpha = 0.75, size = 1) +
  theme(axis.title.y = element_blank()) +
  labs(
    title = "The typical values for all three kinds of income are increasing",
    subtitle = "Incomes are skewed upward, and much more so for earned income",
    x = "Year",
    color = "Income type",
    linetype = "Statistic",
    caption = str_wrap(
      "Mean and median income received of each type shown for individuals actually receiving that kind of income; individuals for which the given type of income is zero are excluded.",
      width = 65
    )
  ) +
  scale_color_discrete(labels = c("Earned income", "Social insurance income", "Total income")) +
  scale_linetype_discrete(labels = c("Mean", "Median"))

The means are always higher than the medians, showing that the data are skewed upward (there are a few women earning a lot more than the median). Average total income is lower than earned income because it averages across all women receiving income, including those receiving social insurance income in lieu of earned income.

Before diving into education attainment by group over time, let’s look at how education levels have evolved over time as a whole.

Code

women_over_25 |>
  plot_props_over_time(education) +
  labs(
    title = "Higher education levels are rising",
    subtitle = "Having a bachelor's or master's degree is increasingly common",
    color = "Education level"
  )

It’s not surprising to see that education levels are rising; the proportion of women with only a high school diploma has been falling almost monotonically since 1994.

Now, let’s examine education attainment by group.

Code

women_over_25 |>
  plot_mean_by_group_over_time(college_lgl) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Higher education increasing among women over 25",
    subtitle = "Rate of having a college degree has doubled since 1994",
    y = "Proportion with college degree"
  )

Code

women_over_25 |>
  plot_mean_by_group_over_time(college_lgl, race) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Increase in higher education is about equal across race",
    subtitle = "Thus, the racial gap persists over time",
    y = "Proportion with college degree",
    color = "Race"
  )

Code

women_over_25 |>
  plot_mean_by_group_over_time(college_lgl, age) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "The largest increases in education are in older groups",
    subtitle = "The rate for people 75+ has nearly tripled since 1994",
    y = "Proportion with college degree",
    color = "Age group"
  )

The rate of having a college degree is increasing over time for all races and ages. Women aged 75+ seem to have a comparably larger increase in the rate of having a college degree, but the effect seems small.

In conclusion for this question, wages, social insurance income, and education attainment are all increasing for most groups of women during the period since 1994. It is unclear, however, how this might be driving the overall trend in women’s LFP. It does seem like there may be a connection between higher wages and education attainment for older women and their relatively large increase in LFP.

Question 4

Between 1994 and 2024, which year had the steepest increase in female labor force participation relative to the previous year? What factors do you think are driving this pattern?

Let’s chart the year-over-year changes in female LFP and observe which bar is the highest. Let’s also create a table that shows the three years with the largest increases in LFP and the three years with the largest decreases in LFP.

Code

women_lfp_rate_chg <- women |>
  summarize(
    .by = year,
    lfp_rate = weighted.mean(lfp_lgl, wgt)
  ) |>
  mutate(
    lfp_rate_chg = lfp_rate - lag(lfp_rate)
  ) |>
  select(!lfp_rate) |>
  filter(year != 1994) |>
  arrange(desc(lfp_rate_chg))

women_lfp_rate_chg |>
  ggplot(aes(x = year, y = lfp_rate_chg)) +
  geom_col() +
  labs(
    title = "Large changes in LFP seem to line up with economic cycles",
    subtitle = "The recessions in 2000, in 2008, and during COVID all seem to appear here",
    y = "Change in LFP rate",
    x = "Year"
  ) +
  scale_y_continuous(labels = percent)

Code

bind_rows(
  slice_head(women_lfp_rate_chg, n = 3),
  slice_tail(women_lfp_rate_chg, n = 3),
) |>
  gt() |>
  fmt_percent(lfp_rate_chg) |>
  cols_align(align = "left", columns = year) |>
  cols_label(
    year = "Year",
    lfp_rate_chg = "Change in LFP rate"
  ) |>
  opt_align_table_header(align = "left") |>
  tab_options(table.align = "left") |>
  tab_header(
    title = "The year with the steepest increase in female LFP was 1997",
    subtitle = "This may line up with the peak of the economic boom of the 1990s, which was followed by the 2000s recession; the largest decline in 2021 also roughly lines up with COVID"
  )

The year with the steepest increase in female LFP was 1997
This may line up with the peak of the economic boom of the 1990s, which was followed by the 2000s recession; the largest decline in 2021 also roughly lines up with COVID
Year	Change in LFP rate
1997	1.09%
2023	0.65%
2007	0.61%
2013	−0.54%
2015	−0.64%
2021	−0.99%

Both the chart and the table confirm that 1997 was the year with the largest increase. This probably lines up with boom/bust cycles, with 1997 being the boom leading up to the recession of the early 2000s.

Question 5

How has labor force participation for college-educated and not college-educated women evolved since 1994?

Let’s chart it.

Code

women |>
  plot_mean_by_group_over_time(lfp_lgl, college) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Women with a college education have higher LFP rates",
    subtitle = "However, the decline in LFP for college-educated women has also been higher",
    y = "Labor force participation rate",
    color = "College status"
  )

College-educated women experienced a slightly larger drop in LFP since 1994 than women without a college education. Furthermore, the drop for college-educated women has been more or less monotonic, with a steady decrease almost every year. The LFP rate for women without a college education, however, initially rose until the early 2000s, after which it experienced a sharper decline.

Question 6

Create an alternative measure of labor force participation that excludes individuals from the labor force if they are self-employed in their main job. Using the new measure, describe how labor force participation for college-educated and not college-educated women has evolved since 1994.

Let’s create the alternative measure and compare it to the standard measure by college education status over time.

Create function to streamline code

compare_lfp_rates_by_group <- function(data, group) {
  data |>
    summarize(
      .by = c(year, {{ group }}),
      lfp_rate = weighted.mean(lfp_lgl, wgt),
      lfp_rate_excl_self = weighted.mean(lfp_lgl_excl_self, wgt)
    ) |>
    pivot_longer(
      cols = c(lfp_rate, lfp_rate_excl_self),
      names_to = "lfp_type",
      values_to = "lfp_rate"
    ) |>
    ggplot(aes(x = year, y = lfp_rate, color = {{ group }}, linetype = lfp_type)) +
    geom_line(alpha = 0.75) +
    geom_point(alpha = 0.75, size = 1) +
    scale_y_continuous(labels = percent) +
    labs(
      y = "LFP rate",
      x = "Year",
      linetype = "LFP methodology"
    ) +
    scale_linetype_discrete(labels = c("Standard", "Exclude self-employment"))
}

Code

women |>
  compare_lfp_rates_by_group(college) +
  labs(
    title = "Alternative LFP measure is everywhere lower than the standard",
    subtitle = "Pattern of evolution of LFP by college status is not materially different",
    color = "College status"
  )

The effect of changing to the alternative measure is larger for women with a college degree than for women without a college degree, indicating that a relatively larger proportion of women with a college degree are self-employed. This seems to make sense given that self-employed workers are usually in a skilled or white-collar profession. However, any effects of the recent rise of the gig economy do not seem to be captured in the data, perhaps because they are small relative to the magnitude of the labor force as a whole.

Question 7

How does our labor market analysis change when we use the new measure? Which measure do you prefer? Explain.

Let’s examine the new measure versus the old measure over time for some different cross sections of the data.

Code

women |> compare_lfp_rates_by_group()

Code

women |>
  compare_lfp_rates_by_group(race) +
  facet_wrap(vars(race), ncol = 3) +
  labs(color = "Race")

Code

women |>
  compare_lfp_rates_by_group(age) +
  labs(color = "Age group")

Code

women |>
  compare_lfp_rates_by_group(income_quantiles) +
  labs(color = "Income quintile")

Code

women |>
  compare_lfp_rates_by_group(education) +
  labs(color = "Education level")

Without analyzing the effect of changing to the new measure on different cross sections of the data, it is hard to say for certain, but it seems that the effect is simply to lower LFP across the board, albeit in differing magnitudes for different demographics.

At the end of the day, the way labor force participation should be measured depends on the goals of the economic analyst. If LFP is simply intended to show how much of the population is working, then self-employment should clearly count as employment because self-employed people are indeed working. In this case, due to the differing effects on different demographics, filtering out self-employed people would distort the numbers.

However, there could be applications of LFP for which it makes sense to filter out self-employed people. Perhaps the self-employed are less likely to try to find a new job if they lose work, or are otherwise unwilling to work if not for themselves; then, it might make sense to exclude them if the goal is to use LFP as a proxy for the size of the active labor market.

Part 2: Telework

The most important concept here is that had_telework was created while the data was grouped by cpsidp. This means that any individual who had telework during COVID is categorized as having had telework for all years. The point is to be able to follow the same individuals (who had telework during COVID) and observe their outcomes post-COVID; the years before COVID can be disregarded into missing values later.

There is a flaw in this logic: since the data are presumably grouped, and each row is likely not an individual observation (as evidenced by the wgt variable), this “cohort” analysis will not be perfectly accurate as we may be following cohorts of groups, rather than cohorts of individuals. Since this is an inherent characteristic of the provided data, this problem will be ignored here for the sake of this exercise only.

Question 1

Since the rise of telework in 2020, how have wages, employment, and labor force participation changed for women who had telework from 2020-2024 and women who did not?

It is unclear how telework during 2020 or after year-end 2022 would be inferred from the rest of the variables, so this question will be answered by comparing women who had telework during 2021-2022 due to COVID and women who did not have telework during 2021-2022 due to COVID (using the covid_telework field).

Let’s examine labor market outcomes by teleworking status during COVID.

Create function to streamline code

plot_mean_over_time_by_telework <- function(data, var) {
  data |>
    summarize(
      .by = c(year, had_telework),
      mean = weighted.mean({{ var }}, wgt, na.rm = TRUE),
    ) |>
    mutate(
      had_telework = if_else(year <= 2019, NA, had_telework)
    ) |>
    ggplot(aes(x = year, y = mean, color = had_telework)) +
    geom_line(alpha = 0.75) +
    geom_point(alpha = 0.75) +
    labs(
      x = "Year",
      color = "Had telework?*",
      caption = "* During 2021-2022 due to COVID."
    ) +
    scale_color_discrete(labels = c("No", "Yes", "NA")) +
    coord_cartesian(xlim = c(2016, 2024))
}

Code

women |>
  filter(income != 0) |>
  plot_mean_over_time_by_telework(income) +
  labs(
    title = "Higher income for women who could telework",
    subtitle = "About $40,000 difference in earnings into 2023",
    y = "Mean earned income",
    caption = "Note: Income-earners only.\n* During 2021-2022 due to COVID."
  ) +
  scale_y_continuous(labels = dollar)

Code

women |>
  plot_mean_over_time_by_telework(employed_lgl) +
  labs(
    y = "Employment rate"
  ) +
  scale_y_continuous(labels = percent)

Code

women |>
  plot_mean_over_time_by_telework(lfp_lgl) +
  labs(
    y = "LFP rate"
  ) +
  scale_y_continuous(labels = percent)

Women who were able to telework during COVID had markedly better incomes than women who were not able to telework, with the effect even expanding into 2023 after the pandemic had mostly ended.

The effects on employment and labor force participation are unclear because all respondents where covid_telework was not NA were both employed and in the labor force in that year. In other words, all respondents categorized as “Yes” or “No” in the above charts were both employed and in the labor force when the answer was recorded. When the group/mutate calculations are made by ID, it becomes clear that some women who teleworked in 2021 did not in 2022, and vice versa, causing employment and LFP to be less than 100% in the charts. At face value, it seems that having telework is related to higher employment and labor force participation, but due to the way the data were collected, this conclusion is likely flawed.

Question 2

For which groups of women older than 25 was telework due to the pandemic most common in 2021? Based on these patterns, what can you infer about the relationship between economic well-being and the ability to telework between 2021?

Let’s examine 2021 teleworking rates for women older than 25 by demographic factors.

Create function to streamline code

plot_mean_by_group_in_year <- function(data, var, group, in_year, sort = FALSE) {
  data <- data |>
    filter(year == in_year) |>
    mutate(
      group = {{ group }}
    ) |>
    summarize(
      .by = group,
      mean = weighted.mean({{ var }}, wgt, na.rm = TRUE)
    )
  if (sort) {
    data <- data |> mutate(group = fct_reorder(group, mean))
  }
  data |>
    ggplot(aes(x = mean, y = group)) +
    geom_point()
}

Code

women_over_25 |>
  plot_mean_by_group_in_year(covid_tw_lgl, race, 2021, TRUE) +
  labs(
    title = "AAPI women had highest teleworking rate",
    subtitle = "White and mixed women also had high rates",
    y = "Race",
    x = "Teleworking rate in 2021"
  ) +
  scale_x_continuous(labels = percent)

Code

women_over_25 |>
  plot_mean_by_group_in_year(covid_tw_lgl, age, 2021) +
  labs(
    title = "Younger women teleworked more",
    subtitle = "Monotonic negative relationship between age and teleworking rate",
    y = "Age group",
    x = "Teleworking rate in 2021"
  ) +
  scale_x_continuous(labels = percent)

Code

women_over_25 |>
  plot_mean_by_group_in_year(covid_tw_lgl, education, 2021) +
  labs(
    title = "Educated women teleworked more",
    subtitle = "Women with a college degree had 10-20% higher rates",
    y = "Education level",
    x = "Teleworking rate in 2021"
  ) +
  scale_x_continuous(labels = percent)

Although a direct observation of the relationship between income and teleworking rate is unavailable in the data, the aforementioned demographic factors are highly correlated with income. It can therefore be inferred that women with higher incomes were much more likely to be able to telework in 2021.

Question 3

Predict what trends in wages, employment, and labor force participation for college-educated women from 2020 to 2024 would have looked like if telework was not an option. What does this tell you about the economic impacts of telework during the COVID-19 pandemic?

Let’s examine labor market outcomes for college-educated women by teleworking status during COVID.

Code

women |>
  filter(college == "Has college degree", income != 0) |>
  plot_mean_over_time_by_telework(income) +
  labs(
    title = "Higher income for female graduates who could telework",
    subtitle = "Those who could not telework had the same income as those with no response",
    y = "Mean earned income",
    caption = "Note: Income-earning college graduates only.\n* During 2021-2022 due to COVID."
  ) +
  scale_y_continuous(labels = dollar)

Code

women |>
  filter(college == "Has college degree") |>
  plot_mean_over_time_by_telework(employed_lgl) +
  labs(
    y = "Employment rate",
    caption = "Note: College graduates only.\n* During 2021-2022 due to COVID."
  ) +
  scale_y_continuous(labels = percent)

Code

women |>
  filter(college == "Has college degree") |>
  plot_mean_over_time_by_telework(lfp_lgl) +
  labs(
    y = "LFP rate",
    caption = "Note: College graduates only.\n* During 2021-2022 due to COVID."
  ) +
  scale_y_continuous(labels = percent)

Under this simple analysis (not attempting to consider a counterfactual), telework caused positive economic impacts for college-educated women during and after the pandemic. Under the assumption that the complete absence of telework during COVID would have caused all college-educated women to experience the same effects as those college-educated women without telework actually experienced during COVID, we can say that the difference between the blue and red lines in the above charts represents the impacts of telework. Incomes were higher, employment rates were slightly higher, and LFP rates were higher as a result of telework. Of course, this analysis is caveated in the same way as in the answer to Question 1.

Notes

The dataset used is from the U.S. Current Population Survey.

The data task instructions are from PREDOC. The version of the instructions reproduced on this page includes minor edits for relevance and presentation.